Classical Chinese poetry is a jewel in the treasure house of Chinese culture.
Previous poem generation models only allow users to employ keywords to
interfere the meaning of generated poems, leaving the dominion of generation to
the model. In this paper, we propose a novel task of generating classical
Chinese poems from vernacular, which allows users to have more control over the
semantic of generated poems. We adapt the approach of unsupervised machine
translation (UMT) to our task. We use segmentation-based padding and
reinforcement learning to address under-translation and over-translation
respectively. According to experiments, our approach significantly improve the
perplexity and BLEU compared with typical UMT models. Furthermore, we explored
guidelines on how to write the input vernacular to generate better poems. Human
evaluation showed our approach can generate high-quality poems which are
comparable to amateur poems.

本文提出了一种利用非文言文生成古典诗歌的新任务，并采用了无监督机器翻译的方法，在语义上给予用户更多控制权，通过基于分词的填充和强化学习等方法，取得了很好的实验效果，同时探索了如何提高生成诗歌的输入口诀，得到了高质量的诗歌生成。